| Title |
Classification of Melanoma Presence and Thickness Based on Computational Image Analysis |
| Authors |
SÁNCHEZ MONEDERO, JAVIER, Saez, Aurora , PÉREZ ORTIZ, MARÍA, Antonio Gutierrez, Pedro , Hervas-Martinez, Cesar |
| External publication |
No |
| Means |
Lecture Notes in Computer Science |
| Scope |
Proceedings Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.339 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964047348&doi=10.1007%2f978-3-319-32034-2_36&partnerID=40&md5=b261632185eccb4789dc32a8ad8f5764 |
| Publication date |
01/01/2016 |
| ISI |
000389499600036 |
| Scopus Id |
2-s2.0-84964047348 |
| DOI |
10.1007/978-3-319-32034-2_36 |
| Abstract |
Melanoma is a type of cancer that occurs on the skin. Only in the US, 50,000-100,000 patients are yearly diagnosed with melanoma. Five year survival rate highly depends on early detection, varying between 99% and 15% depending on the melanoma stage. Melanoma is typically identified with a visual inspection and lately confirmed and classified by a biopsy. In this work, we propose a hybrid system combining features which describe melanoma images together with machine learning models that learn to distinguish melanoma lesions. Although previous works distinguish melanoma and non-melanoma images, those works focus only in the binary case. Opposed to this, we propose to consider finer classification levels within a five class learning problem. We evaluate the performance of several nominal and ordinal classifiers using four performance metrics to provide highlights of several aspects of classification performance, achieving promising results. |
| Keywords |
Melanoma; Feature extraction; Dermoscopic image; Computer vision; Machine learning; Multi-class; Ordinal classification; Imbalanced classification |
| Universidad Loyola members |
|
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